class_docstr: Head of DETR. DETR:End-to-End Object Detection with Transformers.
  
  More details can be found in the `paper
  <https://arxiv.org/pdf/2005.12872>`_ .
  
  Args:
      num_classes (int): Number of categories excluding the background.
      embed_dims (int): The dims of Transformer embedding.
      num_reg_fcs (int): Number of fully-connected layers used in `FFN`,
          which is then used for the regression head. Defaults to 2.
      sync_cls_avg_factor (bool): Whether to sync the `avg_factor` of
          all ranks. Default to `False`.
      loss_cls (:obj:`ConfigDict` or dict): Config of the classification
          loss. Defaults to `CrossEntropyLoss`.
      loss_bbox (:obj:`ConfigDict` or dict): Config of the regression bbox
          loss. Defaults to `L1Loss`.
      loss_iou (:obj:`ConfigDict` or dict): Config of the regression iou
          loss. Defaults to `GIoULoss`.
      train_cfg (:obj:`ConfigDict` or dict): Training config of transformer
          head.
      test_cfg (:obj:`ConfigDict` or dict): Testing config of transformer
          head.
      init_cfg (:obj:`ConfigDict` or dict, optional): the config to control
          the initialization. Defaults to None.
docstr: Perform forward propagation of the head, then calculate loss and
  predictions from the features and data samples. Over-write because
  img_metas are needed as inputs for bbox_head.
  
  Args:
      hidden_states (tuple[Tensor]): Feature from the transformer
          decoder, has shape (num_decoder_layers, bs, num_queries, dim).
      batch_data_samples (list[:obj:`DetDataSample`]): Each item contains
          the meta information of each image and corresponding
          annotations.
  
  Returns:
      tuple: the return value is a tuple contains:
  
      - losses: (dict[str, Tensor]): A dictionary of loss components.
      - predictions (list[:obj:`InstanceData`]): Detection
        results of each image after the post process.
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code: def loss_and_predict(
      self, hidden_states: Tuple[Tensor], batch_data_samples: SampleList
  ) -> Tuple[dict, InstanceList]:
      """Perform forward propagation of the head, then calculate loss and
      predictions from the features and data samples. Over-write because
      img_metas are needed as inputs for bbox_head.
  
      Args:
          hidden_states (tuple[Tensor]): Feature from the transformer
              decoder, has shape (num_decoder_layers, bs, num_queries, dim).
          batch_data_samples (list[:obj:`DetDataSample`]): Each item contains
              the meta information of each image and corresponding
              annotations.
  
      Returns:
          tuple: the return value is a tuple contains:
  
          - losses: (dict[str, Tensor]): A dictionary of loss components.
          - predictions (list[:obj:`InstanceData`]): Detection
            results of each image after the post process.
      """
      batch_gt_instances = []
      batch_img_metas = []
      for data_sample in batch_data_samples:
          batch_img_metas.append(data_sample.metainfo)
          batch_gt_instances.append(data_sample.gt_instances)
  
      outs = self(hidden_states)
      loss_inputs = outs + (batch_gt_instances, batch_img_metas)
      losses = self.loss_by_feat(*loss_inputs)
  
      predictions = self.predict_by_feat(
          *outs, batch_img_metas=batch_img_metas
      )
      return losses, predictions
  
imports: from typing import Dict, List, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import Linear
  from mmcv.cnn.bricks.transformer import FFN
  from mmengine.model import BaseModule
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures import SampleList
  from mmdet.structures.bbox import bbox_cxcywh_to_xyxy, bbox_overlaps, bbox_xyxy_to_cxcywh
  from mmdet.utils import ConfigType, InstanceList, OptInstanceList, OptMultiConfig, reduce_mean
  from ..losses import QualityFocalLoss
  from ..utils import multi_apply
label_desc: Use image node_id 9 for calling the DETRHead obj's underlying member method code with example usage: obj.loss_and_predict(hidden_states, batch_data_samples) and returns: losses,  predictions